AIMC Topic: Retrospective Studies

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Integrating intratumoral and peritumoral radiomics with deep transfer learning for DCE-MRI breast lesion differentiation: A multicenter study comparing performance with radiologists.

European journal of radiology
PURPOSE: To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy o...

Predicting the Efficacy of Repeated Shockwave Lithotripsy for Treating Patients with Upper Urinary Tract Calculi Using an Artificial Neural Network Model.

Urology journal
PURPOSE: To establish a prediction model for repeated shockwave lithotripsy (SWL) efficacy to help choose an appropriate treatment plan for patients with a single failed lithotripsy, reducing their treatment burden.

The relationship between design-based lateralization, humeral bearing design, polyethylene angle, and patient-related factors on surgical complications after reverse shoulder arthroplasty: a machine learning analysis.

Journal of shoulder and elbow surgery
BACKGROUND: Technological advancements in implant design and surgical technique have focused on diminishing complications and optimizing performance of reverse shoulder arthroplasty (rTSA). Despite this, there remains a paucity of literature correlat...

Time-Series MR Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Pathological complete response (pCR) is an essential criterion for adjusting follow-up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short-term memory (VGG-LSTM) network using time-s...

Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs.

Journal of pediatric surgery
BACKGROUND: Radiographic diagnosis of necrotizing enterocolitis (NEC) is challenging. Deep learning models may improve accuracy by recognizing subtle imaging patterns. We hypothesized it would perform with comparable accuracy to that of senior surgic...

Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentation Mastopexy.

Aesthetic plastic surgery
BACKGROUND: Single-stage mastopexy augmentation is a much-debated intervention due to its complexity and the associated relatively high complication rates. This study aimed to reevaluate the risk factors for these complications using a novel approach...

Computed Tomography Effective Dose and Image Quality in Deep Learning Image Reconstruction in Intensive Care Patients Compared to Iterative Algorithms.

Tomography (Ann Arbor, Mich.)
Deep learning image reconstruction (DLIR) algorithms employ convolutional neural networks (CNNs) for CT image reconstruction to produce CT images with a very low noise level, even at a low radiation dose. The aim of this study was to assess whether t...

Predictive approach for liberation from acute dialysis in ICU patients using interpretable machine learning.

Scientific reports
Renal recovery following dialysis-requiring acute kidney injury (AKI-D) is a vital clinical outcome in critical care, yet it remains an understudied area. This retrospective cohort study, conducted in a medical center in Taiwan from 2015 to 2020, enr...

Artificial intelligence-based radiographic extent analysis to predict tuberculosis treatment outcomes: a multicenter cohort study.

Scientific reports
Predicting outcomes in pulmonary tuberculosis is challenging despite effective treatments. This study aimed to identify factors influencing treatment success and culture conversion, focusing on artificial intelligence (AI)-based chest X-ray analysis ...